Isham and Medley [1]}; Wallinga et al. [2]}; Farrington at al. [3]} contend that it is necessary to account for individual heterogeneities while modelling the transmission of an infectious disease. The unstructured homogeneously mixing epidemic model (Model U) introduced by Lamprinakou et al. [4]} is a step toward developing epidemic models considering individual heterogeneities and revealing underlying dynamics. In this paper, working in this direction, we suggest a novel age-stratified epidemic model considering a finite population (Model A) and using a marked latent Hawes process for the infections. We propose a KDPF to infer the marked counting process and forecast the epidemic's future trajectory over short time horizons. We demonstrate the performance of the proposed algorithm on COVID-19.
